Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors of An Introduction to Generalized Linear Models, extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

Maximum Likelihood Estimation

Maximum likelihood estimation is based on the conceptually appealing notion that the estimated parameters—in our case, estimated regression parameters—should be those that maximize the value of the density function specified for the sample data. That is, conditional on the sample data, maximum likelihood estimation finds the parameter values that most likely ...

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